In practical digital communication systems, the frequency response of the underlying channel from the transmitter to the receiver is seldom known at the receiver side. For example, in digital communication over the dial-up telephone network, the communication channel will be different every time a number is dialed, because the channel route will be different. In this example, the characteristics of the channel are unknown a priori. There are other types of channels, e.g. wireless channel such as radio channels and underwater acoustic channels, whose frequency response characteristics are time varying. Thus coherent communications for such channels require the utilization of adaptive algorithms, known as “channel estimators”, for tracking/estimating the varying characteristics of the channel.
Traditionally, channel estimators are divided into two categories: data aided and non-data aided (blind) estimators. Data aided channel estimators operate on a pre-specified set of transmitted symbols that are known to the receiver. These symbols do not convey any information and are often called “pilot symbols” or “training sequences”. Data aided channel estimators are typically simple to implement and relatively robust. Their major disadvantage is that they lead to an overall reduction in system throughput, since some of the transmitted symbols (the pilot symbols) do not carry any information.
Non data aided channel estimators, on the other hand, do not reduce the system throughput. However, they are typically quite complicated to implement as they are often based on higher order moments/cumulants of the received signal, and they most often suffer from high statistical variability, i.e. they suffer from large estimation errors.
The article “Maximum A Posteriori Multipath Fading Channel Estimation for CDMA Systems” by Mohamed Siala and Daniel Duponteil, Proceedings of Vehicular Technology Conference, Houston, Tex., May, 1999, describes a channel estimation algorithm which combines both approaches. This algorithm uses both pilot and data symbols to construct a channel estimator. However, this algorithm requires that the joint statistical probability distribution of the channel multipaths be known to the receiver. In practice, a complete statistical description of the channel characteristics is seldom known to the receiver. Moreover, these characteristics may be time varying.